Hello Robert, I am trying to simulate a rectangular block with the fixed bottom and loaded at the top by an area force.
I have following doubts. Please clarify 1.How to consider the area force. 2.Which elastic equation should I use? I am not finding any equation for area loads. 3.I'm sharing my problem description file. Kindly look into it and help me understand. I am very new to SfePy.
from sfepy.mechanics.matcoefs import lame_from_youngpoisson
from sfepy.discrete.fem.utils import refine_mesh
from sfepy import data_dir
# Tell SfePy to use our .mesh file
filename_mesh = data_dir + '/meshes/3d/block.mesh'
# Tell SfePy where u want to save the output
output_dir = '.'
# Material parameters.
young = 200000.0 # Young's modulus [MPa]
poisson = 0.26 # Poisson's ratio
options = {
'output_dir' : output_dir,
}
#Regions are used to define the boundary conditions, the domains of terms and materials etc.
regions = {
'Omega' : 'all',
'Bottom' : ('vertices in (y=0)', 'facet'),
'Top':('vertices in (y=1)', 'facet'),
}
#Define constitutive parameters (e.g. stiffness, permeability, or viscosity),
#Also other non-field arguments of terms (e.g. known traction or volume forces).
materials = {
'Steel' : ({
'lam' : lame_from_youngpoisson(young, poisson)[0],
'mu' : lame_from_youngpoisson(young, poisson)[1],
},),
'Load' : ({'.val' : [0.0, -5.0,0.0]},),
}
#FE field.Here 'real' is datatype,'3' is dof per node, field is defined over omega & '1' is approximation order
fields = {
'displacement': ('real', '3', 'Omega', 1),
}
#Here we use linear elastic spring equation
equations = {
'balance_of_forces' :
"""dw_lin_elastic_iso.2.Omega(Steel.lam, Steel.mu, v, u )
= dw_point_load.0.Top(Load.val, v)""",
}
# Specify the variables that use the FE approximation given by the specified field
variables = {
'u' : ('unknown field', 'displacement', 0),
'v' : ('test field', 'displacement', 'u'),
}
#Since the bottom is fixed corresponding nodal displacement are zero
ebcs = {
'Fixed' : ('Bottom', {'u.all' : 0.0}),
}
#In SfePy, a non-linear solver has to be specified even when solving a linear problem.
#The linear problem is/should be then solved in one iteration of the nonlinear solver
solvers = {
'ls' : ('ls.scipy_direct', {}),
'newton' : ('nls.newton', {
'i_max' : 1,# Number of iterations
'eps_a' : 1e-6,
}),
} Thanks in advance, Nayan M